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As the performance of single-channel speech separation systems has improved,there has been a desire to move to more challenging conditions than the clean,near-field speech that initial systems were developed on. When training deeplearning separation models, a need for ground truth leads to training onsynthetic mixtures. As such, training in noisy conditions requires either usingnoise synthetically added to clean speech, preventing the use of in-domain datafor a noisy-condition task, or training using mixtures of noisy speech,requiring the network to additionally separate the noise. We demonstrate therelative inseparability of noise and that this noisy speech paradigm leads tosignificant degradation of system performance. We also propose anSI-SDR-inspired training objective that tries to exploit the inseparability ofnoise to implicitly partition the signal and discount noise separation errors,enabling the training of better separation systems with noisy oracle sources.

Training Noisy Single-Channel Speech Separation With Noisy Oracle Sources: A Large Gap and A Small Step